Introduction to Data Analytics

3 Credits · Level: Graduate, Undergraduate

This course teaches students how to synthesize disparate, possibly unstructured data to better understand and characterize the world around us, and in some cases, to draw meaningful inferences. The course design will be centered around different data analytics and data science cases. Topics covered include: the history of data science, successes and failures in data analytics, the data analytics life cycle, formulating data science problems, data wrangling, data characterization (correlations, identifying clusters and associations), data inference, basic network analysis, and visual analytics. This course will use Python and R.